Reconstruction of a surface electrocardiogram based upon an endocardial electrogram

ABSTRACT

The reconstruction of a surface electrocardiogram based upon an endocardial electrogram. This method includes: (a) acquisition ( 10 ) of a plurality of endocardial electrogram signals (EGM) through a plurality of endocardial leads defined based upon endocardial electrodes; (b) calculation ( 12 ), by combining the endocardial electrogram (EGM) signals acquired at step (a), of the corresponding endocardial vectogram (VGM); (c) angular rescaling ( 14 ) of the orthonormalized mark of the endocardial vectogram (VGM) with that of the surface vectocardiogram (VCG); (d) estimation ( 16 ), based upon the endocardial vectogram (VGM) calculated at step (b), of a reconstructed surface vectocardiogram (VCGreconstructed), and (e) calculation ( 18 ) of the surface electrocardiogram (ECG) corresponding to said reconstructed surface vectocardiogram (VCGreconstructed).

FIELD OF THE INVENTION

The present invention relates to “active implantable medical devices” assuch devices are defined by the Jun. 20, 1990 Directive 90/385/CEE ofthe Council of the European Community, and particularly to theimplantable devices that continuously monitor the cardiac rhythm anddeliver to the heart, when necessary, electrical pulses for pacing,resynchronization, cardioversion and/or defibrillation in the case of arhythm disorder that is detected by such device. The invention moreparticularly relates to processing the signals representative of cardiacdepolarization potentials of the myocardium, such signals beingcollected through epicardial or endocardial electrodes for pacing,sensing or defibrillation of the atria or right and left ventricles, ofthese implantable devices. Even more particularly, the present inventionis directed to a method for the reconstruction of a surfaceelectrocardiogram (ECG) starting from an endocardial or epicardialelectrogram (EGM).

BACKGROUND OF THE INVENTION

It is known that EGM signals can be collected by use of electrodesplaced on endocardial or epicardial leads that are implanted with thedevice. These signals, directly correlated to the electrical activity ofcardiac cells, provide much useful information for the purpose ofassessing the patient's condition. Hence, after amplifying, digitizingand filtering, they are mainly utilized to control the cardiac pacer anddiagnose some rhythm disorders requiring, for example, automatictriggering of an antitachycardia, antibradicardia, or interventricularresynchronization therapy, through implementing advanced analysis anddecision taking algorithms.

However, when it comes to analyzing the heart rhythm in a subjectiveway, in order to perform a diagnostic or readjust the parameters of animplanted device, the order to perform a diagnostic or readjust theparameters of an implanted device, the practitioners prefer, inpractice, to interpret the information given by the surfaceelectrocardiogram (ECG). An ECG allows one to visualize in a directmanner, a certain number of determining factors (QRS width, etc.) andthereby weigh the evolution of a heart failure.

Indeed, the ECG and EGM signals, though they actually have the samesource (the electrical activity of myocardium), visually appear in muchdifferent manners: the EGM collected by the implantable device provideslocal information on the electrical activity of a group of heart cells,whereas the ECG appears in the form of more global information,influenced by the propagation of the electrical signal between themyocardium and body surface, and by a certain number of morphologic andpathologic specificities. Thus, the display of EGM signals is not veryuseful to a practitioner who is used to interpreting surface ECGsignals.

It is also usually the ECG signals that are recorded over a long periodof time through ambulatory practice by Holter recorders, so as to befurther processed and analyzed in order to evaluate the clinicalcondition of the patient and eventually diagnose whether a heart rhythmdisorder is present.

Hence, when a patient implanted with a medical device comes to hispractitioner for a routine visit, the practitioner uses two distinctdevices: an ECG recorder and an external implant programmer. In order tocollect the ECG signal, the practitioner places a certain number ofelectrodes on the patient's torso, so as to define the usual twelveuseful leads corresponding to as many distinct ECG signals. As to theexternal programmer, it is used to control certain operating parametersof the implantable device (for example, the battery life), download datafrom the implantable device memory, and eventually to modify theparameters thereof, or upload an updated version of the device operatingsoftware, etc.

The visit with the practitioners therefore usually requires twodifferent devices, as well as specific manipulations for placing thesurface electrodes and collecting the ECG signals.

Moreover, the use of these two devices requires the patient to come to aspecifically equipped center, usually having the consequence of routinevisits that are spaced farther apart, resulting in a less rigorousfollow-up of the patient.

In order to overcome such drawbacks, some algorithms for reconstructinga surface ECG based upon EGM signals (that is from the signals directlyprovided by the implantable device) have been developed. Indeed, thereconstruction of the surface ECG based upon EGM signals would allow:

-   -   to avoid, during routine visits, having to place surface        electrodes and resort to an ECG recorder;    -   to therefore render the visit simpler and quicker, eventually        allow performing the routine visit at the patient's home, and        subsequently shorten the intervals between successive visits,        and improve the patient's follow-up; and to eventually allow a        remote transmission of the EGM data recorded by the implanted        device, without the intervention of a practitioner or medical        aid.

Various algorithms for surface ECG reconstruction based upon EGM signalshave been proposed so far. U.S. Pat. No. 5,740,811 (Hedberg, et al.)proposes to synthesize a surface ECG signal by combining a plurality ofEGM signals by means of a neural network and/or fuzzy logic and/orsummer circuit, after learning performed by an algorithm of the“feedforward” type. Such technique, operating through a linearunidimensional filtering, has the drawback of producing an output signalthat is corresponding to only one lead of the surface ECG, and thereforeonly provides the practitioner with a very narrow vision of thepatient's cardiac activity, compared to the usual twelve leads providedby an external ECG recorder. Another drawback of such technique is thatit does not take into account the position of the endocardial leads,which may change between the moment of the learning process and that ofthe use of the device, a change in the heart electrical axis will havethe effect of biasing the synthesized ECG signal, which will no longerbe meaningful, with a risk to mask the heart disorder which may then notbe diagnosed.

U.S. Pat. No. 6,980,850 (Kroll, et al.) proposes to overcome thisdifficulty, by proposing a method of surface ECG reconstructionimplementing a matrix transform allowing to render each of the surfaceECG leads individually. Such transform also allows to take into accountseveral parameters, such as patient's respiratory activity or posture,which influence tracking the position of the endocardial leads throughspace. The proposed reconstruction consists of transforming, through apredetermined transfer matrix, an input vector representative of aplurality of EGM signals into a resulting vector representative of thedifferent ECG leads. The transfer matrix is learned through averagingplural instant matrices based upon ECG and EGM vectors recordedsimultaneously over a same period of time along a learning phase.

Although this technique brings an improvement to that proposed in theprevious cited patent, it nevertheless presents certain drawbacks.First, it makes the assumption there exists a linear relationshipbetween ECG and EGM vectors: such an approximation, though relativelyaccurate with patients presenting a regular rhythm, leads in some casesto important errors of ECG reconstruction in the presence of atypical orirregular signal morphologies—corresponding precisely to potentiallypathologic cases. Moreover, the parameters of the transfer matrix aredetermined during a learning phase corresponding to a patient conditionat a given moment. Such a situation may no longer be representativeseveral weeks or months later, notably due to the evolution of thepatient's pathology; such evolution will not be taken into account bythe algorithm, except if the patient is requested to come again to aclinical center for a recalibration of the algorithm (calculation of anew transfer matrix).

Other approaches have also been proposed, such as that described in USpatent application US 2005/0288600 (Zhang et al.), which consists ofusing, instead of EGM signals (which require the use of electrodesplaced on endocardial electrodes), some subcutaneous ECG signalscollected by means of a reduced number of electrodes directly placed onthe surface of the implanted device's case. The ECG is then directlyobtained from the inside of the patient's body instead of being obtainedfrom surface electrodes applied on the skin, as with standard ECGrecorders. The collected different subcutaneous ECG signals are splitand undergo an analysis (morphology, time intervals, frequentialanalysis) as a function of criteria stored in a memory. The result ofthis analysis is compared to a reference that has been previouslymemorized and updated by the system, notably when some changes occur.The analysis of the signals allows to follow the evolution of thepatient's heart rhythm so as to perform a cardiac diagnostic.

However, making electrodes on the surface of a case is not easy to dofrom a technological point of view.

OBJECTS AND SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a way toovercome the aforementioned drawbacks, by proposing a surface ECGreconstruction of all the leads as they can be presented to apractitioner when he uses a standard ECG recorder, while taking intoaccount the following constraints:

-   -   parameters specific to the patient including: orientation and        size of the heart, thickness of biological tissues, etc.;    -   nature and evolution of the pathology;    -   type of implanted device (pacemaker, defibrillator or        resynchronizer, or dual, triple or quadruple-chamber device,        etc.); and    -   tracking of endocardial leads localization.

Moreover, the invention provides the following advantages:

-   -   a method that is partially and totally independent of the        patient,    -   a reconstructed ECG close to an actual ECG,    -   a method that does not require too much computing power,    -   possible implementation in the implantable device, an external        programmer, a home monitoring monitor or a remote data server,        based upon data remotely transmitted thereto.

Broadly, the present invention is directed to a reconstruction methodthat is based upon a vectorial approach involving estimating the surfaceECG not directly from the endocardial EGM, but by using an intermediatetridimensional vectorial representation of the various ECG or EGMsignals, respectively.

More particularly, the present invention is directed to a methodincluding the following successive steps:

-   -   a) acquisition of a plurality of endocardial electrogram signals        through a plurality of endocardial or intravascular leads        (located in blood vessels of the heart), defined based upon said        endocardial electrodes;    -   b) calculation, through combination of the endocardial        electrogram signals acquired at step a), of a corresponding        endocardial vectogram;    -   d) estimation, based upon the endocardial vectogram calculated        at step b), of a reconstructed surface vectocardiogram; and    -   e) calculation of the surface electrocardiogram signals        corresponding to said reconstructed surface vectocardiogram.        Step b) of the endocardial vectogram calculation preferably may        comprise an orthogonalization processing, notably requiring a        Karhunen-Loeve transform.

In a preferred embodiment, a step optionally may also be added, betweensteps b) and d), of angular resealing the orthonormated mark of theendocardial vectogram upon that of the surface vectocardiogram.Determination of angular resealing parameters may be performed with apreliminary calibration phase through the following sub-steps:

-   i) obtaining a set of reference data through simultaneous    acquisition of endocardial electrogram signals and surface    electrocardiogram signals;-   ii) calculation, through combination of the surface    electrocardiogram signals acquired at step i), of the corresponding    surface vectocardiogram;-   iii) calculation, through combination of endocardial electrogram    signals acquired at step i), of the corresponding endocardial    vectogram;-   iv) angular resealing of the orthonormated mark of the endocardial    vectogram upon that of the surface vectocardiogram;-   v) estimation, based upon the endocardial vectogram calculated at    step iii), of a reconstructed surface vectocardiogram; and-   vi) adjustment of the angular resealing parameters of step iv) so as    to minimize the deviation between the surface vectocardiogram    calculated at step ii) and the reconstructed surface vectocardiogram    calculated at step v). Determining angular resealing parameters can    preferably be implemented through an adaptive neural network.

In a preferred embodiment step d) of estimation of the reconstructedsurface vectocardiogram can comprise a non-linear filtering applied tothe endocardial vectogram as calculated through step b), preferably afiltering implemented by an adaptive neural network.

The parameters of non-linear filtering can be determined during apreliminary calibration phase through the following sub-steps:

-   -   i) obtaining a set of reference data through simultaneous        acquisition of endocardial electrogram signals and surface        electrocardiogram signals;    -   ii) calculation, through combination of the surface        electrocardiogram signals acquired at step i), of the        corresponding surface vectocardiogram;    -   iii) calculation, through combination of endocardial electrogram        signals acquired at step i), of the corresponding endocardial        vectogram;    -   iv) possible angular rescaling of the orthonormated mark of the        endocardial vectogram upon that of the surface vectocardiogram;    -   v) estimation, through applying non-linear filtering to the        endocardial vectogram calculated at step iii), of a        reconstructed surface vectocardiogram; and    -   vi) adjustment of the non-linear filtering parameters of step v)        so as to minimize the deviation between the surface        vectocardiogram calculated at step ii) and the reconstructed        surface vectocardiogram calculated at step v).

The non-linear filtering may also receive as input one parameterselected from among the group consisting of: a respiratory signal;information on the position of the endocardial sensing electrodes; aphase of the cardiac cycle, e.g., P, QRS or T; and an endocardialimpedance signal, e.g., representative of a thoracic volume orrespiration.

The endocardial leads used for the acquisition of endocardialelectrogram signals are typically defined based upon a plurality ofelectrodes chosen from among the group consisting of: right ventriculardistal electrode and/or right ventricular proximal electrode; rightatrial distal electrode and/or right atrial proximal electrode; leftventricular distal electrode and/or left ventricular proximal electrode;ventricular or atrial defibrillation coil, and supraventriculardefibrillation coil.

Another aspect of the present invention is directed towards an apparatusfor reconstructing an electrocardiogram based upon electrogram signalsacquired by an implantable medical device, in which the functionality ofthe aforementioned method steps is implemented in a microprocessor basedmachine having memory and control algorithms for performing thefunctions. In this regard, in one embodiment, one such apparatus forprocessing signals representative of cardiac myocardium depolarizationpotentials acquired by a plurality of endocardial electrodes of anactive implantable medical device of the implantable pacemaker,resynchronization, cardioversion and/or defibrillation type, includes:

-   -   a) means for acquiring a plurality of endocardial electrogram        signals (EGM) representative of a plurality of endocardial or        intravascular leads;    -   b) means for calculating, based on a combination of said        acquired endocardial electrogram signals (EGM), a corresponding        endocardial vectogram (VGM);    -   c) means for estimating, based upon the calculated endocardial        vectogram, a reconstructed surface vectocardiogram        (VCGreconstructed); and    -   d) means for calculating surface electrocardiogram signals (ECG)        corresponding to said reconstructed surface vectocardiogram        (VCGreconstructed).

Preferably, the means for calculating the endocardial vectogram (VGM)further comprises means for performing an orthogonalization process,more preferably by performing a Karhuen-Loeve transform of saidcombination of EGM signals. As noted in the foregoing method, theapparatus also may perform an angular resealing of the orthonormatedmark of the endocardial vectogram (VGM) upon that of the surfacevectocardiogram (VCG) prior to estimating said reconstructed surfacevectocardiogram. Further, the apparatus may determine the parameters ofthe angular resealing during a preliminary step of calibration.

It should be understood that the means for performing the variousfunctions of the apparatus of the present invention for producing thereconstructed ECG signals includes the microprocessor, associatedmemory, and associated software for executing software instructions toprocess the acquired electrogram signals and perform the reconstructionof the ECG therefrom as described herein. This results in areconstructed ECG data which a practictioner can then utilize in thesame manner for patient follow-up care as real ECG data would be used.Advantagously, it should be understood that the apparatus for producingthe reconstructed ECG may be employed in any of a number of implantablemedical devices, as well as in devices that are not implanted, but canacquire the EGM signal obtained from a patient, e.g., by an implanteddevice (or by electrodes that are at least temporarily implanted in thepatient).

Yet another aspect of the present invention is directed to a softwarecontrol module (instruction set) that can be installed in an implantabledevice or in a non-implanted device such as a patient monitor or aprogrammer, that can reconstruct ECG signals from acquired EGM signals,as such EGM signals may be provided by an implanted device andtransmitted to such non implanted device, and stored in memory forsubsequent processing. One such software control module includes:

-   -   a) a first instruction set for acquiring a plurality of        endocardial electrogram signals (EGM) representative of a        plurality of endocardial or intravascular leads;    -   b) a second instruction set for calculating, based on a        combination of said acquired endocardial electrogram signals        (EGM), a corresponding endocardial vectogram (VGM);    -   c) a third instruction set for estimating, based on the        calculated endocardial vectogram, a reconstructed surface        vectocardiogram (VCGreconstructed); and    -   d) a fourth instruction set for calculating surface        electrocardiogram signals (ECG) corresponding to said        reconstructed surface vectocardiogram (VCGreconstructed).

Preferably, the software control module also includes an instruction setfor performing an orthogonalization process, more preferably byperforming a Karhuen-Loeve transform of said combination of EGM signals.The software control module also may include instructions for performingan angular resealing of the orthonormated mark of the endocardialvectogram (VGM) upon that of the surface vectocardiogram (VCG) prior toestimating said reconstructed surface vectocardiogram as described inthe above mentioned method. Further, the instruction set may perform apreliminary calibration sequence for determining the parameters of theangular resealing and nonlinear filtering and an adaptive neutralnetwork.

It should be understood that the software control module also canimplement the other method steps described herein, and that a person ofordinary skill in the art could employ any of a number of specificinstruction sequences to implement the control software of the presentinvention. Further, it should be understood that such control softwarecan be uploaded to an existing implanted or non implantable machine thatalready acquires EGM signals of a patient, using conventional uploadingtechnology, so as to be able to produce the reconstructed ECG signals inaccordance with the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, advantages and characteristics of the presentinvention will become apparent to a person of ordinary skill in the artin view of the following detailed description of preferred embodimentsof the invention, made with reference to the drawings annexed, in whichlike reference characters refer to like elements, and in which:

FIG. 1 shows, in projection on three planes and in axonometricperspective, the loop described by a typical vectocardiogram (VCG) overa cardiac cycle;

FIG. 2 is a schematic showing the steps of the method of the presentinvention, allowing reconstruction of an electrocardiogram ECG basedupon the electrogram (EGM) signals produced by an implantable device;

FIG. 3 shows, in projection on three planes and in axonometricperspective, the loop described by a typical vectocardiogram (VCG) overthree successive cardiac cycles;

FIG. 4 is a schematic showing the steps implemented during thepreliminary calibration phase, so as to define the parameters forangular resealing to be applied to the VGM;

FIG. 5 is a schematic showing the steps implemented during thepreliminary calibration phase, so as to define the parameters for thenon-linear vectorial filtering allowing to reconstruct the VCG basedupon rescaled VGM; and

FIG. 6 is a comparative example showing the aspect of the reconstructedECG based upon the EGM signals through implementing the presentinvention, comparing to an actual ECG collected by means of surfaceelectrodes following a conventional technique.

DETAILED DESCRIPTION OF THE INVENTION

With reference to FIGS. 1-6, one will now describe a preferredembodiment of a device employing the method, apparatus, and softwarecontrol module in accordance with the present invention. Regarding thesoftware-related aspects thereof, the functionality and processes of thepresent invention can be implemented by an appropriate programming ofthe software of a known implantable pulse generator, for example, apacemaker or defibrillator/cardioverter, comprising means for acquiringa signal provided through endocardial leads.

The invention can preferably be applied to the commercial implantabledevices marketed by ELA Medical, Montrouge France, such as Symphony andELA Rhapsody brand pacemakers and comparable commercial and/orproprietary devices of other manufacturers. These devices are equippedwith programmable microprocessors, including circuits intended toacquire, format and process electrical signals collected by implantedelectrodes and various sensors, and deliver pacing pulses to implantedelectrodes. It is also possible to upload towards these devices, bytelemetry, pieces of software (i.e., a software control module) thatwill be stored in internal memory and run so as to implement thefeatures and functionality of the invention, as described herein.Implementing the features of the invention into these devices isbelieved to be easily feasible by a person of ordinary skill in the art,and will therefore not be described in detail in this document.

The present invention can however be implemented not only within animplantable device (direct processing of the VGM signals), but alsowithin an external programmer used by a practitioner, so as to downloadand analyze the heart signals collected and memorized by an implantabledevice. In a preferred embodiment, the present invention is alsoimplemented in a home monitoring monitor, which is a particular type ofexternal programmer, the operation of which is typically totallyautomated and does not require the intervention of a practitioner,notably to allow remote transmission towards a distant site at regularintervals, for example, on a daily basis, of the collected data, forfurther analysis and patient follow-up. The present invention canequally be implemented level with the data server of said distant remotesite, the raw EGM data thus being directly uploaded towards this server,with no preliminary processing and stored in memory therein. Theprocessing can then be performed by the server or terminal (PC orprogrammer) connected thereto.

In a general manner, cardiac electrical activity is manifesting itselfon the surface of the patient's body through signals that are said to beelectrocardiographic (ECG), that are collected between two pairs ofelectrodes applied on determined locations on the patient's thorax, eachpair of electrodes determining a vector. The whole constitutes a set oftwelve vectors, in such a way that cardiac electrical activity can beassimilated to twelve-dimensional representation that is varying intime. The bipolar leads (I, II, III) and unipolar leads (aVF, aVR, aVL)allow to represent the electrical activity in the frontal plane, whileprecordial leads (v1 to v6) represent the electrical activity in thehorizontal plane.

The surface electrical activity can also be represented in atridimensional graphical format by a vector in coordinates x, y, z as ithas been proposed by E. Frank, An Accurate, Clinically Practical Systemfor Spatial Vectocardiography, Circulation, 13:737-749, May 1956. Such arepresentation, called vectocardiogram (VCG), can be obtained from a setof seven surface electrodes, and contains all the information on themyocardium depolarization and repolarization processes, that is to sayit is as much complete, in terms of information, as the twelveunidimensional ECG signals.

The VCG is thus presented as a vector, the module and direction of which(by reference to the bench mark defined by the patient's thorax) beingconstantly varying in time. The tip of this vector, at each heart beat,is describing a loop that is visually representing the patient's cardiacactivity. FIG. 1 shows an example of such a loop described by the tip ofthe VCG vector along one cardiac cycle. The VCG loop is represented inperspective (FIG. 1 a) and in projection onto three planes, respectivelyfrontal, horizontal and sagittal (FIGS. 1 b to 1 d).

It has also been demonstrated, as notably described in U.S. Pat. No.4,850,370 (Dower), that it is possible to reduce the system of Frank,which used to comprise seven electrodes, to a system with only fourelectrodes, called EASI system. Two transform matrices have also beendefined, called “Dower matrix” and “inverse Dower matrix”, allowing theretrieval of the twelve ECG leads (unidimensional signals) based uponthe VCG, and conversely. In other words, this document describes abiunivocal transform between a plurality (twelve) of ECG signals merelydefined in the time domain, and the variations within that same timedomain, of the tridimensional and vectorial graphical representation ofthe vectocardiogram (VCG).

The basic idea behind the present invention is the reconstructing thesurface ECG lead signals starting from the endocardial EGM signalscollected by the implanted device, through an intermediate vectorialtransform implying a reconstruction of the VCG. Essentially, one aspectof the present invention is directed to a method for:

-   -   collecting the endocardial electrogram (EGM) signals,    -   constructing the corresponding vectogram (VGM),    -   transforming said vectogram (VGM) into a vectocardiogram (VCG),        and    -   reconstructing the surface electrogram (ECG) signals based upon        the VCG by means of an inverse Dower matrix (or any other        technique leading to similar results).

These different steps are shown on the schematics of FIG. 2, with thefollowing successive steps:

-   -   Step 10: selection of the EGM electrodes of the implant allowing        for collection the EGM signals that will allow construction of a        corresponding endocardial vectogram,    -   Step 12: construction of the endocardial vectogram VGM,        tridimensional representation of the cardiac vector based upon        the electrogram (EGM) signals. The vectogram VGM has three        orthogonal components providing the propagation of cardiac        activity in three planes x, y, z and its components are        calculated by an orthogonalization, by using for example a        Karhunen-Loeve transform (see below).    -   Step 14: construction of a resealed vectogram VGMrescaled,        through a rotation M(θ) of the vectogram providing        VGMrescaled=M(θ)·V GM, so as to make the orthonormalized mark of        the resealed vectogram VGMrescaled, to that of the        vectocardiogram.    -   Step 16: estimation of the reconstructed vectocardiogram        VCGreconstructed based upon the resealed vectogram, with        VCGreconstructed =W·VGMrescaled.    -   Step 18: calculation of the twelve ECG vectors through applying        the inverse Dower matrix D, with ECG=D·VCGreconstructed.        Each of these steps will be described in more detail. below.

The purpose is to acquire a plurality of endocardial EGM signals basedupon a corresponding plurality of leads corresponding to pairs ofendocardial electrodes connected to the implanted device's case.

The choice for the electrodes constituting these leads depends upon thetype of implanted device to be considered: pacemaker (for the treatmentof bradycardiae), defibrillator (for the treatment of tachycardiae andfibrillations) or resynchronization device (for the treatment of heartfailure). Further, three pacing modes are distinguished: single, dual ortriple-chamber. To these different features are corresponding differentelectrodes, and a different number of EGM signals according thereto.

If “RV”, “RA” and “LV” respectively stand for the right ventricular,right atrium and left ventricular electrodes of the endocardial leads,with “+” or “−” indicating the respective distal or proximal locationsof the electrode, and “CoilV” and “SVC” stand for the electrodes forventricular and supraventricular fibrillation respectively, then thepossible electrode combinations are the following (with, for each ofthem, the possibility to define a lead between the two electrodes, orbetween one of them and the implantable pulse generator case):

-   -   single chamber: RV+, RV− (and CoilV in the case of a        defibrillator); a single chamber pulse generator can thus        provide two EGM signals thanks to the distal and proximal        electrodes, the ground being corresponding to the case. The        “defibrillator” version can provide three EGM signals, thanks to        the additional CoilV electrode.    -   dual chamber: RV+, RV−, RA+, RA− (and CoilV and SVC in the case        of a defibrillator); a dual-chamber pulse generator can thus        provide four EGM signals, and six in the case of a        defibrillator.    -   triple chamber: RV+, RV−, RA+, RA−, LV+, LV− (and CoilV and SVC        in the case of a defibrillator); a triple-chamber pulse        generator can thus provide six EGM signals, and eight in the        case of a defibrillator.

The present invention is preferably implemented in dual-chamber ortriple-chamber implantable devices, so as to allow recording theelectrical depolarization in the three dimensions, preferably byincluding among the selected EGM signals, the RV unipolar lead and theunipolar and bipolar RA leads.

The present invention is preferably implemented in dual-chamber ortriple-chamber implantable devices, so as to allow recording theelectrical depolarization in the three dimensions, preferably byincluding among the selected EGM signals, the RV unipolar derivation andthe unipolar and bipolar RA derivations.

Construction of the Vectogram VGM

The purpose of this step (step 12 in FIG. 2) is to construct anorthogonal basis based upon a set of electrogram signals. For theimplementation of this invention, it is preferable to use, amongdifferent possible algorithms, the discrete Karhunen-Loeve transform(KLT).

The principle of Karhunen-Loeve algorithm is as follows:

-   Let X_(EGM) be the matrix whose N components EGM₁ to EGM_(N) are the    electrogram vectors corresponding to the isolated beats collected    through the endocardial electrodes (for a better legibility, the    temporal factor t is on purpose not referred to here).    The EGM_(i) signals are normalized based upon the signal with the    highest energy and of a duration approximately equal to that of the    wave to be reconstructed.

One can then define the real and symmetric covariance matrix C_(X)_(EGM) _(X) _(EGM) , that is:C _(X) _(EGM) _(X) _(EGM) =E(X _(EGM) X _(EGM) ^(T))And its associated matrix of eigenvectors A such as:C_(X) _(EGM) _(X) _(EGM) A=ΛA,Where the matrix M(θ) is constituted of the N eigenvalues λ_(i) of C_(X)_(EGM) _(X) _(EGM) .The transform given by:Y=AX_(EGM)=[Y₁ . . . Y_(i) . . . Y_(N)]^(T)is called the discrete Karhunen-Loeve transform (KLT). Its covariancematrix CYY is:C _(YY) =E(YY ^(T))=E(AX _(EGM) X _(EGM) ^(T) A ^(T))=AC _(X) _(EGM)_(X) _(EGM) A ^(T)The CYY matrix has therefore the same eigenvalues as the C_(X) _(EGM)_(X) _(EGM) matrix, and the Yi vectors are represented in an orthogonalmark whose axes are the principal axes of the EGMi vectors (theeigenvectors of C_(X) _(EGM) _(X) _(EGM) ) and constitute thereconstructed vectogram, hereinafter referred to as VGM.

Experimentation has shown that when applying a Karhunen-Loeve transformto all collected EGM signals, three components contain more than 90% ofthe information. It is then observed that the estimated VGM has theaspect of an ellipse in the space of the three principal components.FIG. 3 shows an example of VGM thus reconstructed, for three successiveheartbeats. In this figure, the VCG loop is represented in perspective(FIG. 3 a) and in projection on the three principal planes (FIGS. 3 b to3 d), directions of which are determined by a principal componentanalysis algorithm.

Angular adjustments shall however be performed so that the axes of thevectogram vectors and that of the vectocardiogram have the samesupports. That requires to proceed (step 14 of FIG. 2) to the rotationof the basis formed by the principal components of the vectogram VGM.

If the three angles θx, θy and θz are known, then the resealed vectogramVGMrescalcd can be calculated as:VGMrescaled=M(θ)·VGM

Where the rotation matrix M(θ) is defined by:

M(Θ) = M_(x)(θ_(x)) ⋅ M_(y)(θ_(y)) ⋅ M_(z)(θ_(z)) with:${M_{x}\left( \theta_{x} \right)} = \begin{bmatrix}1 & 0 & 0 \\0 & {\cos\left( \theta_{x} \right)} & {- {\sin\left( \theta_{x} \right)}} \\0 & {\sin\left( \theta_{x} \right)} & {\cos\left( \theta_{x} \right)}\end{bmatrix}$ ${M_{Y}\left( \theta_{Y} \right)} = \begin{bmatrix}0 & 1 & 0 \\{- {\sin\left( \theta_{y} \right)}} & 0 & {\cos\left( \theta_{y} \right)} \\{- {\sin\left( \theta_{y} \right)}} & 0 & {\cos\left( \theta_{y} \right)}\end{bmatrix}$ ${M_{Z}\left( \theta_{Z} \right)} = \begin{bmatrix}{\cos\left( \theta_{z} \right)} & {- {\sin\left( \theta_{z} \right)}} & 0 \\{\sin\left( \theta_{z} \right)} & {\cos\left( \theta_{z} \right)} & 0 \\0 & 0 & 1\end{bmatrix}$

On a practical point of view, the three angles θx, θy and θz areunknown. They have to be learned through a learning basis constituted ofEGM electrograms and ECG electrocardiograms acquired simultaneously atthe moment of the implantation. FIG. 4 shows the different steps of thispreliminary learning phase allowing to define the parameters of theangular rescaling.

The first step consists of simultaneously acquiring the correspondingEGM and ECG signals (step 20). The EGM signals are utilized toreconstruct a corresponding VCG (VCG_(reconstructed)), reconstructedthrough the steps 22, 24 and 26 which are similar to the steps 12, 14and 16 described above in reference to FIG. 2.

In parallel, the ECG signals are processed (step 28) through a Dowertransform, or similar transform allowing to produce the correspondingVCG (VCG_(real)) directly coming from collected ECG signals.

The two VCG (real and reconstructed) are then correlated, and the anglesθx, θy and θz estimated so as to minimize, through least squaresfitting, the mean-square error ε²:

$ɛ^{2} = {\min\limits_{M,\tau}{{{VCG} - {{M(\Theta)} \cdot {VGM}_{r} \cdot J_{\tau}}}}^{2}}$Where J_(τ) allows to preserve the temporal synchronizing of thevectocardiographic loops.

Estimation of the Vectocardiogram VCG

The reconstructed VCG VCG_(reconstructed) can then be calculated (step16 of FIG. 2) through transforming the rescaled VGM (VGM_(rescaled)),preferably through applying a non-linear vectorial filtering, that is:

$\begin{matrix}{{VCG}_{reconstructed} = \begin{bmatrix}{VCG}_{recx} & {VCG}_{recy} & {VCG}_{recz}\end{bmatrix}^{T}} \\{= {W \cdot {VGM}_{rescaled}}}\end{matrix}$ Or: $\begin{matrix}{{VCG}_{reconstructed} = \begin{bmatrix}{VCG}_{recx} & {VCG}_{recy} & {VCG}_{recz}\end{bmatrix}^{T}} \\{= {\quad{\begin{bmatrix}W_{X} & 0 & 0 \\0 & W_{Y} & 0 \\0 & 0 & W_{Z}\end{bmatrix} \cdot \begin{bmatrix}{VGM}_{recx} & {VGM}_{recy} & {VGM}_{recz}\end{bmatrix}^{T}}}}\end{matrix}$

The matrix W can be approximated by a neural network, for example of thesame type as that described in U.S. Pat. No. 5,740,811 (Hedberg et al.)cited above. This neural network is parameterized during the samelearning phase as that which served for determining the parameters ofangular resealing to be applied to the VGM.

A neural network, for example, of the “spiking” type, can perform thefeatures represented in 24 and 26, after a learning trying to minimizethrough least squares fitting, the mean-square error ε². Such a networkis for instance described by Rom et al., Adaptative CardiacResynchronization Therapy Device Based on Spiking Neurons Architecturewith Reinforcement Learning Scheme, Classical Conditioning and SynapticPlasticity, PACE 2005; 28: 1168-1173, November 2005.

This parameterizing is shown by FIG. 5. The first step consists ofsimultaneously collecting the corresponding EGM and ECG signals (step30).

The EGM signals are utilized to reconstruct a corresponding VCG(VCG_(reconstructed)), reconstructed through the steps 32, 34 and 36which are similar to the steps 12, 14 and 16 described above inreference to FIG. 2.

In parallel, the ECG signals are processed (step 38) through a Dowertransform, or similar transform allowing to produce the correspondingVCG (VCG_(real)) directly coming from collected ECG signals.

The two VCG (real and reconstructed) are then correlated, and thefiltering parameters 36 are estimated so as to minimize, through leastsquares fitting, the mean-square error.

After this learning phase, the matrices M(θ) and W are fixed, but mayalso be updated by the practitioner if he wishes.

A neural network, for example of the “spiking” type, can equally performthe features represented in 34 and 36, after a learning trying tominimize through least squares fitting, the mean-square error ε².

Estimation of the ECG Signals B8

The twelve leads of the surface ECG ECGreconstructed are estimated (step18 of FIG. 2) following the principle defined by Dower, that is byapplying the inverse Dower matrix D such as:ECGreconstructed=D·VCGreconstructed.

FIG. 6 provides a superimposed representation of an example of ECGreconstructed based upon EGM signals by utilizing a neural networkaccording to the present invention, compared to the real ECG directlycollected by electrodes placed on the patient's body.

ECG reconstruction through the method of the present invention allows avery good approximation of the real ECG, even for heartbeats with verydifferent morphologies, which makes a difference when comparing notablyto the existing techniques of ECG reconstruction by linear filteringsuch as those proposed by U.S. Pat. No. 6,980,850 (Kroll et al.) citedabove, which leads to unsatisfactory results in the case of atypicalmorphologies of the cardiac signal.

The ECG reconstruction method following the present invention can beapplied, if need be, separately and independently to the P, QRS and Twaves of the cardiac signal, so as to allow a specific analysis.

Finally, as shown in FIG. 2 by phantom line arrows leading to block 16,the vectorial filtering allowing to reconstruct the VCG based upon theVGM can receive as input, some additional parameters likely to modifythe electrical propagation of the cardiac activity between themyocardium and the patient's body surface, such as: position of theelectrodes, phase of the respiratory cycle, and/or variation of thethoracic volume (for example measured by a transthoracic impedancemeasurement).

Although the detailed description of the invention has been discussed inthe context of a method, it should be understood that the presentinvention applies equally to an apparatus and to a software controlmodule that operates on EGM data to perform the functionality of themethod steps to obtain reconstructed ECG data. Indeed, one skilled inthe art will appreciate that the present invention can be practical byother than the embodiments described herein, which are presented forpurposes of illustration and not of limitation.

1. A process for processing signals representative of cardiac myocardiumdepolarization potentials, said signals being collected by a pluralityof endocardial electrodes of an active implantable medical device suchas an implantable pacemaker, device for resynchronization, cardioversionand/or defibrillation, the process being performed by one of: the activeimplantable medical device, an external programmer, data server, theprocess comprising the steps of: a) acquiring a plurality of endocardialelectrogram signals (EGM) through a plurality of leads, each lead of theplurality of leads being collected between at least two pairs ofendocardial electrodes selected from the plurality of endocardialelectrodes; b) calculating, through a combination of the plurality ofendocardial electrogram signals (FGM) acquired at step a), acorresponding endocardial vectogram (VGM); d) estimating, based upon theendocardial vectogram calculated at step b), a reconstructed surfacevectocardiogram (VCGreconstructed); and e) reconstructing surfaceelectrocardiogram signals (ECG) corresponding to said reconstructedsurface vectocardiogram (VCGreconstructed).
 2. The process of claim 1,wherein step b) of calculating the endocardial vectogram (VGM) furthercomprises performing an orthogonalization process.
 3. The process ofclaim 2, wherein performing said orthogonalization process furthercomprises performing a Karhuen-Loeve transform of said combination ofEGM signals.
 4. The process of claim 1, further comprising, betweensteps b) and d): c) angular rescaling the orthonormalized mark of theendocardial vectogram (VGM) upon that of the surface vectocardiogram(VCG).
 5. The process of claim 4, further comprising a preliminary stepof calibration comprising determining parameters of said angularrescaling.
 6. The process of claim 5, wherein determining angularrescaling parameters further comprising the steps of: i) obtaining a setof reference data through simultaneous acquisition of endocardialelectrogram signals (EGM) and surface electrocardiogram signals (ECG);ii) calculating, through a combination of the surface electrocardiogramsignals (ECG) acquired at step i), a corresponding surfacevectocardiogram (VCGreal); iii) calculating, through a combination ofendocardial electrogram signals (EGM) acquired at step i), acorresponding endocardial vectogram (VGM); iv) angular rescaling theorthonormalized mark of the endocardial vectogram (VGM) upon that of thesurface vectocardiogram (VCGreal); v) estimating, based upon theendocardial vectogram (VGM) calculated at step iii), of a reconstructedsurface vectocardiogram (VCGreconstructed); and vi) adjusting theangular rescaling parameters of step iv) so as to minimize a deviationbetween the surface vectocardiogram (VCGreal) calculated at step ii) andthe reconstructed surface vectocardiogram (VCGreconstructed) calculatedat step v).
 7. The process of claim 6, wherein said deviation to beminimized is the root-mean-square deviation.
 8. The process of claim 6,further comprising providing an adaptive neural network for adetermination of said angular rescaling parameters.
 9. The process ofclaim 1, wherein, the step d) of estimating the reconstructed surfacevectocardiogram (VCGreconstructed) further comprises applying anon-linear filtering to the endocardial vectogram calculated at step b).10. The process of claim 9, further comprising providing an adaptiveneural network to implement said non-linear filtering,
 11. The processof claim 9, further comprising a preliminary calibration step comprisingdetermining parameters of said non-linear filtering.
 12. The process ofclaim 11, wherein determining non linear filtering parameters furthercomprising the steps of: i) obtaining a set of reference data throughsimultaneous acquisition of endocardial electrogram signals (EGM) andsurface electrocardiogram signals (ECG); ii) calculating, through acombination of the surface electrocardiogram signals (ECG) acquired atstep i), a corresponding surface vectocardiogram (VCGreal); iii)calculating, through combination of endocardial electrogram signals(EGM) acquired at step i), a corresponding endocardial vectogram (VGM);iv) conducting a possible angular rescaling of an orthonormalized markof the endocardial vectogram (VGM) upon that of the surfacevectocardiogram (VCGreal); v) estimating, through applying non-linearfiltering to the endocardial vectogram calculated at step iii), areconstructed surface vectocardiogram (VCGreconstructed); and vi)adjusting the non-linear filtering parameters of step v) so as tominimize a deviation between the surface vectocardiogram (VCGreal)calculated at step ii) and the reconstructed surface vectocardiogram(VCGreconstructed) calculated at step v).
 13. The process of claim 9,further comprising providing to said non-linear filtering as an input,at least one parameter selected from among: at respiratory signal,information on the position of the endocardial sensing electrodes, theP, QRS or T phase of the cardiac cycle; and a signal representative ofintracardiac impedance.
 14. The process of claim 1, wherein saidintracardiac signals are collected using at least one of: rightventricular distal and/or proximal electrode, right atrial distal and/orproximal electrode, left ventricular distal and/or proximal electrode,ventricular or atrial defibrillation coil, and supra-ventriculardefibrillation coil.
 15. The process of claim 1, wherein the pluralityof endocardial electrodes of the active implantable medical devicecomprises two to four endocardial electrodes implanted to a patient. 16.Apparatus for processing signals representative of cardiac myocardiumdepolarization potentials, comprising: a) means for acquiring aplurality of endocardial electrogram signals (EGM) representative of aplurality of leads; b) means for calculating, based on a combination ofsaid acquired plurality of endocardial electrogram signals (EGM), acorresponding endocardial vectogram (VGM); c) means for estimating,based upon the calculated endocardial vectogram, a reconstructed surfacevectocardiogram (VCGreconstructed); and d) means for calculating surfaceelectrocardiogram signals (ECG) corresponding to said reconstructedsurface vectocardiogram (VCGreconstructed).
 17. The apparatus of claim16, wherein said means for calcu1ating the endocardial vectogram (VGM)further comprises means for performing an orthogonalization process. 18.The apparatus of claim 17, wherein said means for performing saidorthogonalization process further comprises means for performing aKarhuen-Loeve transform of said combination of EGM signals.
 19. Theapparatus of claim 16, further comprising: means for angular rescalingan orthonormalized mark of the endocardial vectogram (VGM) upon that ofthe surface vectocardiogram (VCG) prior to estimating said reconstructedsurface vectocardiogram.
 20. The apparatus of claim 19, furthercomprising means for determining parameters of said angular rescalingduring a preliminary calibration.
 21. The apparatus of claim 16,wherein, the means for, estimating the reconstructed surfacevectocardiogram (VCGreconstructed) further comprises means for applyinga non-linear filtering to the calculated endocardial vectogram, saidmeans for nonlinear filtering further comprising a neural network. 22.The apparatus of claim 16, wherein the plurality of endocardialelectrodes of the active implantable medical device comprises two tofour endocardial electrodes implanted to a patient.
 23. A non-transitorysoftware control module for processing signals representative of cardiacmyocardium depolarization potentials, comprising: a) a first instructionset for acquiring a plurality of endocardial electrogram signals (EGM)representative of a plurality of leads; b) a second instruction set forcalculating, based on a combination of said acquired endocardialelectrogram signals (EGM), a corresponding endocardial vectogram (VGM);c) a third instruction set for estimating, based upon the calculatedendocardial vectogram, a reconstructed surface vectocardiogram(VCGreconstructed); and d) a fourth instruction set for calculatingsurface electrocardiogram signals (ECG) corresponding to saidreconstructed surface vectocardiogram (VCGreconstructed).
 24. Thenon-transitory software control module of claim 23, wherein said secondinstruction set further comprises instructions performing anorthogonalization process by performing a Karhuen-Loeve transform ofsaid combination of EGM signals.
 25. The non-transitory software controlmodule of claim 23, further comprising: an instruction set for angularrescaling an orthonormalized mark of the endocardial vectogram (VGM)upon that of the surface vectocardiogram (VCG) prior to estimating saidreconstructed surface vectocardiogram.
 26. The non-transitory softwarecontrol module of claim 23, further comprising an instruction set fordetermining parameters of said angular rescaling during a preliminarycalibration.
 27. The non-transitory software control module of claim 26,wherein the instruction set for determining angular rescaling parametersfurther comprise instructions for: i) obtaining a set of reference datathrough simultaneous acquisition of endocardial electrogram signals(EGM) and surface electrocardiogram signals (ECG); ii) calculating,through a combination of the surface electrocardiogram signals (ECG)acquired at step i), a corresponding surface vectocardiogram (VCGreal);iii) calculating, through a combination of endocardial electrogramsignals (EGM) acquired at step i), a corresponding endocardial vectogram(VGM); iv) angular rescaling the orthonormalized mark of the endocardialvectogram (VGM) upon that of the surface vectocardiogram (VCGreal); v)estimating, based upon the endocardial vectogram (VGM) calculated atstep iii), of a reconstructed surface vectocardiogram(VCGreconstructed); and vi) adjusting the angular rescaling parametersof step iv) so as to minimize the deviation between the surfacevectocardiogram (VCGreal) calculated at step ii) and the reconstructedsurface vectocardiogram (VCGreconstructed) calculated at step v). 28.The non-transitory software module of claim 23, wherein the plurality ofendocardial electrodes of the active implantable medical devicecomprises two to four endocardial electrodes implanted to a patient.